Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Getting Started
- Setup and Installation
TensorFlow Basics
- Creation, Initializing, Saving, and Restoring TensorFlow variables
- Feeding, Reading and Preloading TensorFlow Data
- How to use TensorFlow infrastructure to train models at scale
- Visualizing and Evaluating models with TensorBoard
TensorFlow Mechanics 101
- Prepare the Data
- Download
- Inputs and Placeholders
- Build the Graph
- Inference
- Loss
- Training
- Train the Model
- The Graph
- The Session
- Train Loop
- Evaluate the Model
- Build the Eval Graph
- Eval Output
Advanced Usage
- Threading and Queues
- Distributed TensorFlow
- Writing Documentation and Sharing your Model
- Customizing Data Readers
- Using GPUs
- Manipulating TensorFlow Model Files
TensorFlow Serving
- Introduction
- Basic Serving Tutorial
- Advanced Serving Tutorial
- Serving Inception Model Tutorial
Getting Started with SyntaxNet
- Parsing from Standard Input
- Annotating a Corpus
- Configuring the Python Scripts
Building an NLP Pipeline with SyntaxNet
- Obtaining Data
- Part-of-Speech Tagging
- Training the SyntaxNet POS Tagger
- Preprocessing with the Tagger
- Dependency Parsing: Transition-Based Parsing
- Training a Parser Step 1: Local Pretraining
- Training a Parser Step 2: Global Training
Vector Representations of Words
- Motivation: Why Learn word embeddings?
- Scaling up with Noise-Contrastive Training
- The Skip-gram Model
- Building the Graph
- Training the Model
- Visualizing the Learned Embeddings
- Evaluating Embeddings: Analogical Reasoning
- Optimizing the Implementation
Requirements
Working knowledge of python
35 Hours
Testimonials (3)
Very knowledgeable
Usama Adam - TWPI
Course - Natural Language Processing with TensorFlow
The way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Course - Natural Language Processing with TensorFlow
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
